Overview

Dataset statistics

Number of variables19
Number of observations1564
Missing cells459
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory232.3 KiB
Average record size in memory152.1 B

Variable types

Numeric14
Categorical4
Boolean1

Alerts

Aneu_neck is highly overall correlated with Aneu_width and 7 other fieldsHigh correlation
Aneu_width is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
Aneu_height is highly overall correlated with Aneu_width and 9 other fieldsHigh correlation
Aneu_volume is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
coil_count is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
coil_length1 is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
coil_size1 is highly overall correlated with Aneu_width and 9 other fieldsHigh correlation
coil_size2 is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
coil_length2 is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
coil_size3 is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
coil_length3 is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
Aneu_width_label is highly overall correlated with Aneu_width and 9 other fieldsHigh correlation
Adj_tech has 60 (3.8%) missing valuesMissing
VER has 272 (17.4%) missing valuesMissing
coil_size3 has 53 (3.4%) missing valuesMissing
coil_length3 has 53 (3.4%) missing valuesMissing

Reproduction

Analysis started2023-09-16 06:31:40.946221
Analysis finished2023-09-16 06:32:18.862488
Duration37.92 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Distinct1562
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1345.062
Minimum1
Maximum2515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-09-16T15:32:18.950193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile199.6
Q1741.75
median1390
Q31938.25
95-th percentile2389.85
Maximum2515
Range2514
Interquartile range (IQR)1196.5

Descriptive statistics

Standard deviation699.79417
Coefficient of variation (CV)0.52026908
Kurtosis-1.1381874
Mean1345.062
Median Absolute Deviation (MAD)598
Skewness-0.14635776
Sum2103677
Variance489711.89
MonotonicityNot monotonic
2023-09-16T15:32:19.158705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1564 2
 
0.1%
2426 2
 
0.1%
2 1
 
0.1%
2135 1
 
0.1%
2144 1
 
0.1%
2143 1
 
0.1%
2142 1
 
0.1%
2140 1
 
0.1%
2139 1
 
0.1%
2138 1
 
0.1%
Other values (1552) 1552
99.2%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
8 1
0.1%
11 1
0.1%
16 1
0.1%
17 1
0.1%
20 1
0.1%
27 1
0.1%
30 1
0.1%
ValueCountFrequency (%)
2515 1
0.1%
2514 1
0.1%
2512 1
0.1%
2511 1
0.1%
2510 1
0.1%
2509 1
0.1%
2506 1
0.1%
2504 1
0.1%
2503 1
0.1%
2502 1
0.1%

Sex
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
woman
1178 
man
386 

Length

Max length5
Median length5
Mean length4.5063939
Min length3

Characters and Unicode

Total characters7048
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwoman
2nd rowwoman
3rd rowwoman
4th rowwoman
5th rowwoman

Common Values

ValueCountFrequency (%)
woman 1178
75.3%
man 386
 
24.7%

Length

2023-09-16T15:32:19.352083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:32:19.545943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
woman 1178
75.3%
man 386
 
24.7%

Most occurring characters

ValueCountFrequency (%)
m 1564
22.2%
a 1564
22.2%
n 1564
22.2%
w 1178
16.7%
o 1178
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7048
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 1564
22.2%
a 1564
22.2%
n 1564
22.2%
w 1178
16.7%
o 1178
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 7048
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 1564
22.2%
a 1564
22.2%
n 1564
22.2%
w 1178
16.7%
o 1178
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 1564
22.2%
a 1564
22.2%
n 1564
22.2%
w 1178
16.7%
o 1178
16.7%

Age
Real number (ℝ)

Distinct64
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.943734
Minimum16
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-09-16T15:32:19.719555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile39
Q151
median60
Q367
95-th percentile76
Maximum88
Range72
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.548198
Coefficient of variation (CV)0.19591901
Kurtosis-0.30594599
Mean58.943734
Median Absolute Deviation (MAD)8
Skewness-0.32604421
Sum92188
Variance133.36088
MonotonicityNot monotonic
2023-09-16T15:32:19.929909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 59
 
3.8%
58 59
 
3.8%
66 58
 
3.7%
65 56
 
3.6%
62 55
 
3.5%
57 53
 
3.4%
67 50
 
3.2%
63 49
 
3.1%
69 49
 
3.1%
73 48
 
3.1%
Other values (54) 1028
65.7%
ValueCountFrequency (%)
16 1
 
0.1%
19 1
 
0.1%
20 1
 
0.1%
26 1
 
0.1%
27 2
0.1%
28 3
0.2%
29 2
0.1%
30 1
 
0.1%
31 4
0.3%
32 3
0.2%
ValueCountFrequency (%)
88 1
 
0.1%
87 3
 
0.2%
85 2
 
0.1%
84 2
 
0.1%
82 4
 
0.3%
81 9
0.6%
80 7
0.4%
79 10
0.6%
78 12
0.8%
77 13
0.8%

Aneu_location
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
ICA
1029 
ACA
228 
MCA
145 
BA
116 
VA
 
46

Length

Max length3
Median length3
Mean length2.8964194
Min length2

Characters and Unicode

Total characters4530
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowICA
2nd rowICA
3rd rowICA
4th rowMCA
5th rowICA

Common Values

ValueCountFrequency (%)
ICA 1029
65.8%
ACA 228
 
14.6%
MCA 145
 
9.3%
BA 116
 
7.4%
VA 46
 
2.9%

Length

2023-09-16T15:32:20.140002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:32:20.305593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ica 1029
65.8%
aca 228
 
14.6%
mca 145
 
9.3%
ba 116
 
7.4%
va 46
 
2.9%

Most occurring characters

ValueCountFrequency (%)
A 1792
39.6%
C 1402
30.9%
I 1029
22.7%
M 145
 
3.2%
B 116
 
2.6%
V 46
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4530
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1792
39.6%
C 1402
30.9%
I 1029
22.7%
M 145
 
3.2%
B 116
 
2.6%
V 46
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4530
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1792
39.6%
C 1402
30.9%
I 1029
22.7%
M 145
 
3.2%
B 116
 
2.6%
V 46
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1792
39.6%
C 1402
30.9%
I 1029
22.7%
M 145
 
3.2%
B 116
 
2.6%
V 46
 
1.0%

Aneu_neck
Real number (ℝ)

Distinct112
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4563357
Minimum1.3
Maximum12.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-09-16T15:32:20.495071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile2.5
Q13.475
median4.2
Q35.2
95-th percentile7.2
Maximum12.2
Range10.9
Interquartile range (IQR)1.725

Descriptive statistics

Standard deviation1.4693755
Coefficient of variation (CV)0.3297273
Kurtosis2.0048439
Mean4.4563357
Median Absolute Deviation (MAD)0.8
Skewness1.0630934
Sum6969.709
Variance2.1590644
MonotonicityNot monotonic
2023-09-16T15:32:20.700712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6 64
 
4.1%
4 55
 
3.5%
4.2 54
 
3.5%
4.1 53
 
3.4%
4.4 53
 
3.4%
3.9 52
 
3.3%
3.4 50
 
3.2%
3.5 47
 
3.0%
3.1 47
 
3.0%
4.3 46
 
2.9%
Other values (102) 1043
66.7%
ValueCountFrequency (%)
1.3 1
 
0.1%
1.5 1
 
0.1%
1.6 3
 
0.2%
1.7 3
 
0.2%
1.8 7
0.4%
1.9 3
 
0.2%
2 11
0.7%
2.1 8
0.5%
2.2 10
0.6%
2.27 1
 
0.1%
ValueCountFrequency (%)
12.2 1
 
0.1%
11.8 1
 
0.1%
11 2
0.1%
10.4 1
 
0.1%
10.1 1
 
0.1%
10 1
 
0.1%
9.9 3
0.2%
9.6 1
 
0.1%
9.5 2
0.1%
9.4 1
 
0.1%

Aneu_width
Real number (ℝ)

Distinct133
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2379009
Minimum1.2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-09-16T15:32:20.905295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile3
Q14
median4.9
Q36.1
95-th percentile8.9
Maximum14
Range12.8
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.7921105
Coefficient of variation (CV)0.34214288
Kurtosis1.4863618
Mean5.2379009
Median Absolute Deviation (MAD)1
Skewness1.078226
Sum8192.077
Variance3.2116601
MonotonicityNot monotonic
2023-09-16T15:32:21.126150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5 51
 
3.3%
4 50
 
3.2%
4.7 49
 
3.1%
4.3 48
 
3.1%
4.4 46
 
2.9%
4.1 45
 
2.9%
5 42
 
2.7%
4.9 42
 
2.7%
4.2 41
 
2.6%
3.9 39
 
2.5%
Other values (123) 1111
71.0%
ValueCountFrequency (%)
1.2 1
 
0.1%
1.3 1
 
0.1%
1.4 2
0.1%
1.5 1
 
0.1%
1.8 2
0.1%
2 3
0.2%
2.1 4
0.3%
2.2 4
0.3%
2.3 2
0.1%
2.35 1
 
0.1%
ValueCountFrequency (%)
14 1
 
0.1%
12.8 1
 
0.1%
12.06 1
 
0.1%
12 1
 
0.1%
11.8 1
 
0.1%
11.7 1
 
0.1%
11.6 1
 
0.1%
11.4 2
0.1%
11.3 3
0.2%
11.2 1
 
0.1%

Aneu_height
Real number (ℝ)

Distinct118
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.372546
Minimum1.9
Maximum11.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-09-16T15:32:21.320656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile3.2
Q14.1
median5
Q36.2
95-th percentile9.1
Maximum11.8
Range9.9
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.7418205
Coefficient of variation (CV)0.32420764
Kurtosis1.0223874
Mean5.372546
Median Absolute Deviation (MAD)1
Skewness1.0505772
Sum8402.662
Variance3.0339386
MonotonicityNot monotonic
2023-09-16T15:32:21.566957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4 62
 
4.0%
4.9 53
 
3.4%
5.2 48
 
3.1%
4.6 48
 
3.1%
4.1 48
 
3.1%
4 45
 
2.9%
3.9 44
 
2.8%
3.8 44
 
2.8%
4.2 43
 
2.7%
5.1 43
 
2.7%
Other values (108) 1086
69.4%
ValueCountFrequency (%)
1.9 4
 
0.3%
2 1
 
0.1%
2.1 3
 
0.2%
2.4 2
 
0.1%
2.5 7
0.4%
2.6 2
 
0.1%
2.7 8
0.5%
2.8 5
 
0.3%
2.9 6
 
0.4%
3 15
1.0%
ValueCountFrequency (%)
11.8 2
0.1%
11.6 1
 
0.1%
11.5 1
 
0.1%
11.2 4
0.3%
11.1 1
 
0.1%
11 3
0.2%
10.9 1
 
0.1%
10.8 1
 
0.1%
10.7 4
0.3%
10.6 3
0.2%

Aneu_volume
Real number (ℝ)

Distinct1083
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.89277
Minimum1.5833627
Maximum696.55667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-09-16T15:32:21.796499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.5833627
5-th percentile17.195268
Q137.324923
median61.569643
Q3116.64629
95-th percentile339.94558
Maximum696.55667
Range694.9733
Interquartile range (IQR)79.321367

Descriptive statistics

Standard deviation109.50263
Coefficient of variation (CV)1.0853367
Kurtosis7.767874
Mean100.89277
Median Absolute Deviation (MAD)31.036283
Skewness2.6084472
Sum157796.29
Variance11990.826
MonotonicityNot monotonic
2023-09-16T15:32:22.171795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.58616 7
 
0.4%
41.51865 7
 
0.4%
32.61413333 7
 
0.4%
33.4724 7
 
0.4%
61.46707 6
 
0.4%
40.64102 6
 
0.4%
21.79683333 6
 
0.4%
47.6652 6
 
0.4%
23.7384 6
 
0.4%
65.312 5
 
0.3%
Other values (1073) 1501
96.0%
ValueCountFrequency (%)
1.583362697 1
0.1%
2.76948 1
0.1%
3.231583333 1
0.1%
3.40062 1
0.1%
3.977333333 1
0.1%
4.30808 1
0.1%
5.01144 2
0.1%
5.296133333 1
0.1%
5.8404 1
0.1%
6.018333333 1
0.1%
ValueCountFrequency (%)
696.5566667 2
0.1%
676.81915 1
0.1%
669.80282 1
0.1%
663.62016 1
0.1%
658.34496 1
0.1%
642.3695223 1
0.1%
635.50617 1
0.1%
624.9228 1
0.1%
617.3072533 1
0.1%
610.9812 1
0.1%

Adj_tech
Categorical

Distinct4
Distinct (%)0.3%
Missing60
Missing (%)3.8%
Memory size12.3 KiB
Double cathe
532 
Stent assist
469 
BAT
261 
Simple
242 

Length

Max length12
Median length12
Mean length9.4727394
Min length3

Characters and Unicode

Total characters14247
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSimple
2nd rowSimple
3rd rowBAT
4th rowBAT
5th rowBAT

Common Values

ValueCountFrequency (%)
Double cathe 532
34.0%
Stent assist 469
30.0%
BAT 261
16.7%
Simple 242
15.5%
(Missing) 60
 
3.8%

Length

2023-09-16T15:32:22.384933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:32:22.582317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
double 532
21.2%
cathe 532
21.2%
stent 469
18.7%
assist 469
18.7%
bat 261
10.4%
simple 242
9.7%

Most occurring characters

ValueCountFrequency (%)
t 1939
13.6%
e 1775
12.5%
s 1407
 
9.9%
1001
 
7.0%
a 1001
 
7.0%
l 774
 
5.4%
i 711
 
5.0%
S 711
 
5.0%
D 532
 
3.7%
o 532
 
3.7%
Other values (10) 3864
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11220
78.8%
Uppercase Letter 2026
 
14.2%
Space Separator 1001
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1939
17.3%
e 1775
15.8%
s 1407
12.5%
a 1001
8.9%
l 774
 
6.9%
i 711
 
6.3%
o 532
 
4.7%
h 532
 
4.7%
c 532
 
4.7%
b 532
 
4.7%
Other values (4) 1485
13.2%
Uppercase Letter
ValueCountFrequency (%)
S 711
35.1%
D 532
26.3%
B 261
 
12.9%
A 261
 
12.9%
T 261
 
12.9%
Space Separator
ValueCountFrequency (%)
1001
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13246
93.0%
Common 1001
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1939
14.6%
e 1775
13.4%
s 1407
10.6%
a 1001
 
7.6%
l 774
 
5.8%
i 711
 
5.4%
S 711
 
5.4%
D 532
 
4.0%
o 532
 
4.0%
h 532
 
4.0%
Other values (9) 3332
25.2%
Common
ValueCountFrequency (%)
1001
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1939
13.6%
e 1775
12.5%
s 1407
 
9.9%
1001
 
7.0%
a 1001
 
7.0%
l 774
 
5.4%
i 711
 
5.0%
S 711
 
5.0%
D 532
 
3.7%
o 532
 
3.7%
Other values (10) 3864
27.1%

Is_bleb
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
False
1248 
True
316 
ValueCountFrequency (%)
False 1248
79.8%
True 316
 
20.2%
2023-09-16T15:32:22.876949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

VER
Real number (ℝ)

Distinct264
Distinct (%)20.4%
Missing272
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean26.066931
Minimum5.9
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-09-16T15:32:23.115030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile17.755
Q122.5
median25.7
Q328.9
95-th percentile35.6
Maximum60
Range54.1
Interquartile range (IQR)6.4

Descriptive statistics

Standard deviation5.6553367
Coefficient of variation (CV)0.21695445
Kurtosis2.5294824
Mean26.066931
Median Absolute Deviation (MAD)3.2
Skewness0.68956652
Sum33678.474
Variance31.982833
MonotonicityNot monotonic
2023-09-16T15:32:23.409100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 22
 
1.4%
24.6 19
 
1.2%
26.9 18
 
1.2%
21.8 17
 
1.1%
27.9 16
 
1.0%
23.6 15
 
1.0%
26.4 15
 
1.0%
25.2 15
 
1.0%
24.3 14
 
0.9%
26.7 14
 
0.9%
Other values (254) 1127
72.1%
(Missing) 272
 
17.4%
ValueCountFrequency (%)
5.9 1
0.1%
8.1 1
0.1%
8.4 1
0.1%
8.8 1
0.1%
9.8 1
0.1%
11 1
0.1%
11.3 1
0.1%
11.7 1
0.1%
12.1 1
0.1%
12.4 1
0.1%
ValueCountFrequency (%)
60 1
0.1%
52.9 1
0.1%
52.8 1
0.1%
48.7 2
0.1%
45.3 1
0.1%
44.8 1
0.1%
44.4 1
0.1%
44.3 1
0.1%
44.1 2
0.1%
43.9 1
0.1%

coil_count
Real number (ℝ)

Distinct28
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4673913
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-09-16T15:32:23.689972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median6
Q39
95-th percentile16
Maximum50
Range49
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.145725
Coefficient of variation (CV)0.55517715
Kurtosis9.4848682
Mean7.4673913
Median Absolute Deviation (MAD)2
Skewness2.0312972
Sum11679
Variance17.187036
MonotonicityNot monotonic
2023-09-16T15:32:23.951583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
6 220
14.1%
5 218
13.9%
4 181
11.6%
7 173
11.1%
8 136
8.7%
9 116
7.4%
3 111
7.1%
10 79
 
5.1%
11 75
 
4.8%
12 48
 
3.1%
Other values (18) 207
13.2%
ValueCountFrequency (%)
1 10
 
0.6%
2 43
 
2.7%
3 111
7.1%
4 181
11.6%
5 218
13.9%
6 220
14.1%
7 173
11.1%
8 136
8.7%
9 116
7.4%
10 79
 
5.1%
ValueCountFrequency (%)
50 1
 
0.1%
29 1
 
0.1%
28 1
 
0.1%
26 3
 
0.2%
25 3
 
0.2%
23 2
 
0.1%
22 5
0.3%
21 2
 
0.1%
20 7
0.4%
19 8
0.5%

Aneu_width_label
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
0.0
809 
1.0
513 
2.0
242 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4692
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.0 809
51.7%
1.0 513
32.8%
2.0 242
 
15.5%

Length

2023-09-16T15:32:24.235176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:32:24.485264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 809
51.7%
1.0 513
32.8%
2.0 242
 
15.5%

Most occurring characters

ValueCountFrequency (%)
0 2373
50.6%
. 1564
33.3%
1 513
 
10.9%
2 242
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3128
66.7%
Other Punctuation 1564
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2373
75.9%
1 513
 
16.4%
2 242
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 1564
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4692
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2373
50.6%
. 1564
33.3%
1 513
 
10.9%
2 242
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2373
50.6%
. 1564
33.3%
1 513
 
10.9%
2 242
 
5.2%

coil_length1
Real number (ℝ)

Distinct25
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.77711
Minimum2
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-09-16T15:32:24.702411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median8
Q315
95-th percentile20
Maximum40
Range38
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.8197702
Coefficient of variation (CV)0.54001214
Kurtosis2.6977237
Mean10.77711
Median Absolute Deviation (MAD)2
Skewness1.5651272
Sum16855.4
Variance33.869725
MonotonicityNot monotonic
2023-09-16T15:32:24.924992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
8 437
27.9%
6 263
16.8%
10 228
14.6%
15 215
13.7%
20 138
 
8.8%
4 89
 
5.7%
12 61
 
3.9%
30 44
 
2.8%
7 18
 
1.2%
3 16
 
1.0%
Other values (15) 55
 
3.5%
ValueCountFrequency (%)
2 1
 
0.1%
3 16
 
1.0%
4 89
 
5.7%
6 263
16.8%
7 18
 
1.2%
7.5 1
 
0.1%
8 437
27.9%
9 14
 
0.9%
10 228
14.6%
11 2
 
0.1%
ValueCountFrequency (%)
40 2
 
0.1%
36 1
 
0.1%
33 1
 
0.1%
30 44
 
2.8%
25 13
 
0.8%
24 1
 
0.1%
21 1
 
0.1%
20 138
8.8%
19 1
 
0.1%
17.1 1
 
0.1%

coil_size1
Real number (ℝ)

Distinct14
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.484335
Minimum1.5
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-09-16T15:32:25.168094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile3
Q13
median4
Q35
95-th percentile8
Maximum12
Range10.5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5547929
Coefficient of variation (CV)0.34671648
Kurtosis1.7697749
Mean4.484335
Median Absolute Deviation (MAD)1
Skewness1.185592
Sum7013.5
Variance2.4173808
MonotonicityNot monotonic
2023-09-16T15:32:25.373172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
4 413
26.4%
3 343
21.9%
5 282
18.0%
6 175
11.2%
3.5 63
 
4.0%
4.5 62
 
4.0%
7 60
 
3.8%
8 56
 
3.6%
2.5 41
 
2.6%
2 31
 
2.0%
Other values (4) 38
 
2.4%
ValueCountFrequency (%)
1.5 3
 
0.2%
2 31
 
2.0%
2.5 41
 
2.6%
3 343
21.9%
3.5 63
 
4.0%
4 413
26.4%
4.5 62
 
4.0%
5 282
18.0%
6 175
11.2%
7 60
 
3.8%
ValueCountFrequency (%)
12 2
 
0.1%
10 15
 
1.0%
9 18
 
1.2%
8 56
 
3.6%
7 60
 
3.8%
6 175
11.2%
5 282
18.0%
4.5 62
 
4.0%
4 413
26.4%
3.5 63
 
4.0%

coil_size2
Real number (ℝ)

Distinct14
Distinct (%)0.9%
Missing10
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean3.4971042
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-09-16T15:32:25.569284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12.5
median3
Q34
95-th percentile6
Maximum10
Range9
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.4074214
Coefficient of variation (CV)0.40245339
Kurtosis2.3744544
Mean3.4971042
Median Absolute Deviation (MAD)1
Skewness1.3352481
Sum5434.5
Variance1.9808351
MonotonicityNot monotonic
2023-09-16T15:32:25.783351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 477
30.5%
2 293
18.7%
4 280
17.9%
5 135
 
8.6%
2.5 114
 
7.3%
6 91
 
5.8%
3.5 50
 
3.2%
4.5 36
 
2.3%
8 24
 
1.5%
7 18
 
1.2%
Other values (4) 36
 
2.3%
ValueCountFrequency (%)
1 8
 
0.5%
1.5 17
 
1.1%
2 293
18.7%
2.5 114
 
7.3%
3 477
30.5%
3.5 50
 
3.2%
4 280
17.9%
4.5 36
 
2.3%
5 135
 
8.6%
6 91
 
5.8%
ValueCountFrequency (%)
10 4
 
0.3%
9 7
 
0.4%
8 24
 
1.5%
7 18
 
1.2%
6 91
 
5.8%
5 135
 
8.6%
4.5 36
 
2.3%
4 280
17.9%
3.5 50
 
3.2%
3 477
30.5%

coil_length2
Real number (ℝ)

Distinct24
Distinct (%)1.5%
Missing11
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean7.6364456
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-09-16T15:32:26.052961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q38
95-th percentile20
Maximum36
Range35
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.8751036
Coefficient of variation (CV)0.63839957
Kurtosis6.641095
Mean7.6364456
Median Absolute Deviation (MAD)2
Skewness2.2380312
Sum11859.4
Variance23.766635
MonotonicityNot monotonic
2023-09-16T15:32:26.309040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
6 352
22.5%
4 351
22.4%
8 313
20.0%
10 172
11.0%
3 108
 
6.9%
15 79
 
5.1%
20 55
 
3.5%
12 38
 
2.4%
2 33
 
2.1%
30 24
 
1.5%
Other values (14) 28
 
1.8%
(Missing) 11
 
0.7%
ValueCountFrequency (%)
1 3
 
0.2%
2 33
 
2.1%
2.5 1
 
0.1%
3 108
 
6.9%
3.5 1
 
0.1%
4 351
22.4%
5 2
 
0.1%
5.4 1
 
0.1%
6 352
22.5%
7 2
 
0.1%
ValueCountFrequency (%)
36 1
 
0.1%
30 24
 
1.5%
24 1
 
0.1%
20 55
3.5%
17 1
 
0.1%
15 79
5.1%
13.6 1
 
0.1%
13 3
 
0.2%
12 38
2.4%
11.9 1
 
0.1%

coil_size3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)0.9%
Missing53
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean3.0056254
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-09-16T15:32:26.554711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2666643
Coefficient of variation (CV)0.4214312
Kurtosis2.5295996
Mean3.0056254
Median Absolute Deviation (MAD)1
Skewness1.4047883
Sum4541.5
Variance1.6044385
MonotonicityNot monotonic
2023-09-16T15:32:26.750833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 490
31.3%
3 378
24.2%
4 187
 
12.0%
2.5 131
 
8.4%
5 102
 
6.5%
1.5 61
 
3.9%
6 43
 
2.7%
3.5 43
 
2.7%
1 25
 
1.6%
4.5 24
 
1.5%
Other values (3) 27
 
1.7%
(Missing) 53
 
3.4%
ValueCountFrequency (%)
1 25
 
1.6%
1.5 61
 
3.9%
2 490
31.3%
2.5 131
 
8.4%
3 378
24.2%
3.5 43
 
2.7%
4 187
 
12.0%
4.5 24
 
1.5%
5 102
 
6.5%
6 43
 
2.7%
ValueCountFrequency (%)
9 2
 
0.1%
8 16
 
1.0%
7 9
 
0.6%
6 43
 
2.7%
5 102
 
6.5%
4.5 24
 
1.5%
4 187
12.0%
3.5 43
 
2.7%
3 378
24.2%
2.5 131
 
8.4%

coil_length3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)1.3%
Missing53
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean6.3372601
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.3 KiB
2023-09-16T15:32:26.917010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile15
Maximum30
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.0936579
Coefficient of variation (CV)0.64596652
Kurtosis6.6124205
Mean6.3372601
Median Absolute Deviation (MAD)2
Skewness2.1066211
Sum9575.6
Variance16.758035
MonotonicityNot monotonic
2023-09-16T15:32:27.063564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4 431
27.6%
6 263
16.8%
8 234
15.0%
3 207
13.2%
10 127
 
8.1%
2 93
 
5.9%
15 74
 
4.7%
20 29
 
1.9%
12 21
 
1.3%
30 8
 
0.5%
Other values (10) 24
 
1.5%
(Missing) 53
 
3.4%
ValueCountFrequency (%)
1 7
 
0.4%
2 93
 
5.9%
2.5 1
 
0.1%
3 207
13.2%
3.5 2
 
0.1%
4 431
27.6%
4.5 1
 
0.1%
5 1
 
0.1%
6 263
16.8%
7.5 1
 
0.1%
ValueCountFrequency (%)
30 8
 
0.5%
20 29
 
1.9%
15 74
 
4.7%
12.2 1
 
0.1%
12 21
 
1.3%
11.9 1
 
0.1%
11 2
 
0.1%
10 127
8.1%
9 7
 
0.4%
8 234
15.0%

Interactions

2023-09-16T15:32:15.243215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:42.358725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:44.887440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:47.409554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:49.956968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:52.392814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:54.975658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:57.513348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:00.075889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:02.497624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:05.181908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:07.591180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:10.120049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:12.577268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:15.427928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:42.521829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:45.057328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:47.576297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:50.123563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:52.564099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:55.154028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:57.679148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:00.246002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:02.673921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:05.351026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:07.750845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:10.293636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:12.751356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:15.598024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:42.704294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:45.236965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:47.753634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:50.306275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:52.741772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:55.351695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:57.853927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:00.422489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:02.860185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:05.526280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:07.924980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:10.480102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:12.932644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:15.770643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:42.862613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:45.428825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:47.919707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:50.474794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:52.908253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:55.524242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:58.017226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:00.593547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:03.036042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:05.691493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:08.086065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:10.633722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:13.272314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:15.947209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:43.004394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:45.595890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:48.073449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:50.657214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:53.080379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:55.704448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:58.184364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:00.770515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:03.211486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:05.863152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:08.256844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:10.812051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:13.452499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:16.123666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:43.174015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:45.774253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:48.241649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:50.827798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:53.247895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:55.884647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:58.357761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:00.938217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:03.387858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:06.030636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:08.593061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:10.986104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:13.627904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:16.307539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:43.347237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:45.958071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:48.416350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:51.012641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:53.430691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:56.072105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:58.534441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:01.115918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:03.749328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:06.210204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:08.767368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:11.149779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:13.815617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:16.481283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:43.510237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:46.127132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:48.581326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:51.178100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:53.600273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:56.249128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:58.868901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:01.285742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:03.922240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:06.378811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:08.933470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:11.321849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:13.985310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:16.653583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:43.677634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:46.302099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:48.750525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:51.346213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:53.771185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:56.421819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:59.033202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:01.458600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:04.097605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:06.550970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:09.093931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:11.497521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:14.154774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:16.833922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:43.856722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:46.485387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:48.927676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:51.525095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:54.121298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:56.608882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:59.208350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:01.638029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:04.281777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:06.729089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:09.272165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:11.684701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:14.346457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:17.010330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:44.019929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:46.654237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:49.277560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:51.686921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:54.283752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:56.787577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:59.370592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:01.807101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:04.452296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:06.895483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:09.436688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:11.857657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:14.520151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:17.186707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:44.191269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:46.828146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:49.435124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:51.854336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:54.448278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:56.963742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:59.531778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:01.970001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:04.624692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:07.061414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:09.596804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:12.026184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:14.689514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:17.368732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:44.538104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:47.017845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:49.609373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:52.037225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:54.627175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:57.143077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:59.708655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:02.146293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:04.808209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:07.238107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:09.772675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:12.207988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:14.875753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:17.556366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:44.717853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:47.239085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:49.787924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:52.216376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:54.807494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:57.334921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:59.890421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:02.325898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:04.999316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:07.415312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:09.949710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:12.396465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:32:15.071678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-09-16T15:32:27.429971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
IDAgeAneu_neckAneu_widthAneu_heightAneu_volumeVERcoil_countcoil_length1coil_size1coil_size2coil_length2coil_size3coil_length3SexAneu_locationAdj_techIs_blebAneu_width_label
ID1.000-0.0220.050-0.131-0.230-0.1820.290-0.1660.009-0.111-0.016-0.027-0.041-0.1030.0000.0350.4180.1820.116
Age-0.0221.0000.0860.1020.1000.112-0.0960.1010.0140.0220.0280.0550.0210.0440.1280.0940.0950.0000.072
Aneu_neck0.0500.0861.0000.6580.4030.582-0.0910.5770.5170.4870.5160.5370.5140.5300.0000.0960.2230.0000.481
Aneu_width-0.1310.1020.6581.0000.7070.922-0.0860.7490.7290.7620.7560.7700.7330.7660.0000.0510.0990.0520.901
Aneu_height-0.2300.1000.4030.7071.0000.911-0.1350.6720.6830.7400.7010.7120.6680.7070.0000.0230.0710.0820.525
Aneu_volume-0.1820.1120.5820.9220.9111.000-0.1160.7730.7650.8110.7910.8030.7610.8000.0000.0500.0590.0720.736
VER0.290-0.096-0.091-0.086-0.135-0.1161.0000.0980.1090.0850.0380.0560.0230.0200.0000.0660.1360.0340.074
coil_count-0.1660.1010.5770.7490.6720.7730.0981.0000.5660.6210.6100.6120.6110.6350.0660.0930.0860.0560.520
coil_length10.0090.0140.5170.7290.6830.7650.1090.5661.0000.8700.7550.7670.7020.7330.0690.1050.1130.0000.546
coil_size1-0.1110.0220.4870.7620.7400.8110.0850.6210.8701.0000.8430.8260.7710.8040.0000.0620.0740.0000.604
coil_size2-0.0160.0280.5160.7560.7010.7910.0380.6100.7550.8431.0000.8910.8400.8370.0000.0760.0980.0000.588
coil_length2-0.0270.0550.5370.7700.7120.8030.0560.6120.7670.8260.8911.0000.7790.8450.0310.0840.0990.0000.571
coil_size3-0.0410.0210.5140.7330.6680.7610.0230.6110.7020.7710.8400.7791.0000.8770.0400.0760.1100.0000.561
coil_length3-0.1030.0440.5300.7660.7070.8000.0200.6350.7330.8040.8370.8450.8771.0000.0000.0620.0910.0000.588
Sex0.0000.1280.0000.0000.0000.0000.0000.0660.0690.0000.0000.0310.0400.0001.0000.3020.0820.0000.000
Aneu_location0.0350.0940.0960.0510.0230.0500.0660.0930.1050.0620.0760.0840.0760.0620.3021.0000.1780.1360.094
Adj_tech0.4180.0950.2230.0990.0710.0590.1360.0860.1130.0740.0980.0990.1100.0910.0820.1781.0000.0970.076
Is_bleb0.1820.0000.0000.0520.0820.0720.0340.0560.0000.0000.0000.0000.0000.0000.0000.1360.0971.0000.000
Aneu_width_label0.1160.0720.4810.9010.5250.7360.0740.5200.5460.6040.5880.5710.5610.5880.0000.0940.0760.0001.000

Missing values

2023-09-16T15:32:17.986546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-16T15:32:18.370990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-16T15:32:18.686666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDSexAgeAneu_locationAneu_neckAneu_widthAneu_heightAneu_volumeAdj_techIs_blebVERcoil_countAneu_width_labelcoil_length1coil_size1coil_size2coil_length2coil_size3coil_length3
02woman49ICA2.708.2010.90446.706343NaNnoNaN92.030.08.06.020.05.020.0
13woman54ICA6.006.505.50112.255000NaNnoNaN41.015.07.05.010.04.010.0
28woman63ICA5.0011.0011.00696.556667NaNnoNaN152.030.010.08.030.08.020.0
311woman58MCA4.407.907.50238.757750NaNnoNaN122.012.07.06.020.08.020.0
416woman78ICA5.0011.0011.00696.556667SimplenoNaN152.030.010.010.030.08.020.0
517woman69MCA2.403.303.2017.960800NaNnoNaN20.02.02.52.01.0NaNNaN
630woman55ICA8.409.808.40392.035280SimplenoNaN112.030.010.04.08.04.08.0
732woman57ICA4.404.409.30146.691380NaNnoNaN50.08.04.04.04.03.06.0
833woman44ICA4.434.645.8465.232956BATnoNaN60.08.05.03.06.03.04.0
936woman64ICA5.806.707.80198.283150BATnoNaN81.020.08.04.010.03.08.0
IDSexAgeAneu_locationAneu_neckAneu_widthAneu_heightAneu_volumeAdj_techIs_blebVERcoil_countAneu_width_labelcoil_length1coil_size1coil_size2coil_length2coil_size3coil_length3
15542415woman53ICA6.58.17.6254.509560Stent assistyes28.252.020.07.06.020.05.015.0
15552420woman63MCA4.74.06.979.442000Double catheno21.640.010.05.05.010.04.08.0
15562423woman72ICA4.04.05.149.109600Stent assistno24.450.08.03.03.58.02.54.0
15572426man73ACA7.36.65.4111.909600Stent assistno20.881.08.04.04.08.04.08.0
15582434man66ACA7.07.17.3195.295440Stent assistno43.9132.030.08.08.030.04.015.0
15592464man46ICA3.63.84.740.190953Double catheno27.670.08.04.03.04.02.03.0
15602472man70ACA4.03.93.425.675780Stent assistyes38.050.07.04.03.06.01.03.0
15612498woman66VA6.05.76.0105.598200Stent assistno15.891.010.05.04.08.03.04.0
15622499woman59ICA3.64.33.835.060193Stent assistno27.530.08.04.02.54.02.03.0
15632510woman78ICA4.54.96.390.469680Stent assistno23.080.010.05.04.08.03.56.0